Hwang / Lee / Zhu Handbook of Operations Analytics Using Data Envelopment Analysis

1. Auflage 2016
ISBN: 978-1-4899-7705-2
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 511 Seiten

Reihe: Business and Management

This handbook focuses on Data Envelopment Analysis (DEA) applications in operations analytics which are fundamental tools and techniques for improving operation functions and attaining long-term competitiveness. In fact, the handbook demonstrates that DEA can be viewed as Data Envelopment Analytics. Chapters include a review of cross-efficiency evaluation; a case study on measuring the environmental performance of OECS countries; how to select a set of performance metrics in DEA with an application to American banks; a relational network model to take the operations of individual periods into account in measuring efficiencies; how the efficient frontier methods DEA and stochastic frontier analysis (SFA) can be used synergistically; and how to integrate DEA and multidimensional scaling. In other chapters, authors construct a dynamic three-stage network DEA model; a bootstrapping based methodology to evaluate returns to scale and convexity assumptions in DEA; hybridizing DEA and cooperative games; using DEA to represent the production technology and directional distance functions to measure band performance; an input-specific Luenberger energy and environmental productivity indicator; and the issue of reference set by differentiating between the uniquely found reference set and the unary and maximal types of the reference set. Finally, additional chapters evaluate and compare the technological advancement observed in different hybrid electric vehicles (HEV) market segments over the past 15 years; radial measurement of efficiency for the production process possessing multi-components under different production technologies; issues around the use of accounting information in DEA; how to use DEA environmental assessment to establish corporate sustainability; a summary of research efforts on DEA environmental assessment applied to energy in the last 30 years; and an overview of DEA and how it can be utilized alone and with other techniques to investigate corporate environmental sustainability questions.

Shiuh-Nan Hwang is a Professor in the Department of Business Administration, and Dean of the School of Management at Ming Chuan University, Taiwan. He earned his Ph.D. in Management Science at National Chiao Tung University, an M.S. in Industrial Management at National Cheng Kung University, and a B.S. in Agriculture Economics at National Taiwan University. His research interests include Performance Evaluation and Management, Business Research Methods, and General Management. Hsuan-Shih Lee is a Professor and Vice-President in the Department of Shipping & Transportation Management at the College of Maritime Science and Management, National Taiwan Ocean University, Taiwan. He earned his Ph.D., and M.S. in Information Engineering, and B.S. in Computer Engineering, all at National Chiao Tung University, Taiwan. His research interests include Maritime Management and Policy, Performance management, Management Information Systems, System Analysis and Design, Algorithm Analysis, and Computer Networks. Joe Zhu is Professor of Operations Analytics in the Foisie School of Business, Worcester Polytechnic Institute. He is an internationally recognized expert in methods of performance evaluation and benchmarking using Data Envelopment Analysis (DEA). He has published and co-edited more than 15 books focusing on performance evaluation and benchmarking using DEA. He has more than 18,000 Google Scholar citations with over 100 peer-reviewed articles. He is recognized as one of the top authors in DEA with respect to research productivity, h-index, and g-index. He is an Area Editor of OMEGA, and on the Editorial Board of European Journal of Operational Research, and Computers and Operations Research. He is the Series Associate Editor of International Series in Operations Research and Management Science.

Autoren/Hrsg.

Hwang, Shiuh-Nan

Lee, Hsuan-Shih

Zhu, Joe

Inhaltsverzeichnis

1;Preface;6
2;Contents;10
3;Contributors;12
4;Chapter 1: Ranking Decision Making Units: The Cross-Efficiency Evaluation;15
4.1;1.1 Introduction;15
4.2;1.2 Ranking Methods in DEA;17
4.3;1.3 The Cross-Efficiency Evaluation: The Standard Approach;18
4.4;1.4 The Choice of DEA Weights in Cross-Efficiency Evaluations;20
4.4.1;1.4.1 Ranking Ranges and Cross-Efficiency Intervals;25
4.4.2;1.4.2 Illustrative Example;27
4.5;1.5 The Aggregation of Cross-Efficiencies;30
4.5.1;1.5.1 Illustrative Example (Cont.);33
4.6;1.6 Other Uses;34
4.6.1;1.6.1 Identification of Mavericks and All-Round Performers;34
4.6.2;1.6.2 Classification of DMUs and Benchmarking;35
4.6.3;1.6.3 Fixed Cost and Resource Allocation;35
4.7;1.7 Extensions;36
4.7.1;1.7.1 Cross-Efficiency Evaluation with Directional Distance Functions;36
4.7.2;1.7.2 Cross-Efficiency Evaluation with Multiplicative DEA Models;36
4.7.3;1.7.3 Cross-Efficiency Evaluation Under VRS;37
4.7.4;1.7.4 Fuzzy Cross-Efficiency Evaluation;38
4.7.5;1.7.5 Game Cross Efficiency;39
4.8;1.8 Conclusions;39
4.9;References;40
5;Chapter 2: Data Envelopment Analysis for Measuring Environmental Performance;44
5.1;2.1 Introduction;44
5.2;2.2 Environmental DEA Technology;45
5.3;2.3 Models for Measuring Environmental Performance;48
5.3.1;2.3.1 Environmental Efficiency Index;48
5.3.2;2.3.2 Environmental Productivity Index;50
5.3.3;2.3.3 Other Developments;51
5.4;2.4 Case Study;52
5.4.1;2.4.1 Data;52
5.4.2;2.4.2 Results and Discussions;53
5.4.2.1;2.4.2.1 EEI Analysis;53
5.4.2.2;2.4.2.2 EPI Analysis;58
5.5;2.5 Conclusion;59
5.6;References;61
6;Chapter 3: Input and Output Search in DEA: The Case of Financial Institutions;63
6.1;3.1 Introduction;63
6.2;3.2 Efficiency Modeling in Financial Institutions;65
6.3;3.3 A Case Study: American Banks;67
6.3.1;3.3.1 The Data Set: Three Inputs and Three Outputs;68
6.3.1.1;3.3.1.1 Labor;68
6.3.1.2;3.3.1.2 Physical Capital;68
6.3.1.3;3.3.1.3 Deposits;68
6.3.1.4;3.3.1.4 Interest and Non-interest Income;71
6.3.1.5;3.3.1.5 Loans;71
6.3.2;3.3.2 DEA Specification Searches Using Multivariate Methods;79
6.3.3;3.3.3 Results Visualization and Strategic Pattern Identification;85
6.3.4;3.3.4 Dissecting the Efficiency Score;94
6.4;3.4 Conclusions;95
6.5;References;96
7;Chapter 4: Multi-period Efficiency Measurement with Fuzzy Data and Weight Restrictions;100
7.1;4.1 Introduction;100
7.2;4.2 Crisp Network DEA with Weight Restrictions;102
7.3;4.3 Fuzzy Multi-period Efficiency with Weight Restrictions;106
7.4;4.4 Example;111
7.5;4.5 Conclusion;120
7.6;References;121
8;Chapter 5: Pitching DEA Against SFA in the Context of Chinese Domestic Versus Foreign Banks;123
8.1;5.1 Introduction;123
8.2;5.2 Conceptual Framework;125
8.2.1;5.2.1 Chinese Banking Sector;125
8.2.2;5.2.2 Modeling Performance to Estimate Bank Efficiency;127
8.2.3;5.2.3 Contextual Variables;128
8.3;5.3 Data and Method;129
8.3.1;5.3.1 Data;129
8.3.2;5.3.2 Data Envelopment Analysis (DEA);132
8.3.3;5.3.3 Stochastic Frontier Analysis (SFA);135
8.4;5.4 Results and Analysis;137
8.4.1;5.4.1 Testing for Scale Inefficiency Using DEA;137
8.4.2;5.4.2 Main DEA Results;138
8.4.2.1;5.4.2.1 Core Model (Single-Output BCC-O);138
8.4.2.2;5.4.2.2 Extended Model (Two-Output BCC-O);139
8.4.2.2.1;Overall Potential Improvements Identified by DEA Using the Extended Model;140
8.4.2.2.2;Assessing the Marginal Role of the Output Variables in DEA: Efficiency Contribution Measures (ECM) for the Extended Model;140
8.4.3;5.4.3 SFA Results;142
8.4.3.1;5.4.3.1 Core Model (Single-Output Translog Function);142
8.4.3.2;5.4.3.2 Extended Model (Two-Output Translog Function);146
8.4.4;5.4.4 Comparing DEA and SFA Results;146
8.5;5.5 Concluding Remarks;149
8.6;References;151
9;Chapter 6: Assessing Organizations´ Efficiency Adopting Complementary Perspectives: An Empirical Analysis Through Data Envelop...;154
9.1;6.1 Introduction;155
9.2;6.2 DEA and MDS Methodologies: A Brief Overview;156
9.2.1;6.2.1 The Data Envelopment Analysis Method;156
9.2.2;6.2.2 The Multidimensional Scaling Method;157
9.3;6.3 Data and Selection of Indicators;158
9.3.1;6.3.1 Our Sample;158
9.3.2;6.3.2 Inputs and Outputs Employed in the DEA Analysis;159
9.3.3;6.3.3 Indicators Included in the MDS Analysis;160
9.4;6.4 Studying HEIs´ Efficiency by Means of Data Envelopment Analysis: Results;161
9.5;6.5 Combining DEA and MDS Methodologies: Results;163
9.5.1;6.5.1 Preliminary Insights;163
9.6;6.6 Results;168
9.7;6.7 Concluding Remarks;171
9.8;Appendix: List of Universities Included in the Analysis and Their Acronyms;172
9.9;References;173
10;Chapter 7: Capital Stock and Performance of RandD Organizations: A Dynamic DEA-ANP Hybrid Approach;176
10.1;7.1 Introduction;177
10.2;7.2 Literature Review;179
10.2.1;7.2.1 Current Status of Taiwanese RandD Organizations;179
10.2.2;7.2.2 DEA Applications in RandD Organizations;180
10.3;7.3 Research Design;181
10.3.1;7.3.1 Three-Stage Value-Creation Process of RandD Organizations;181
10.3.2;7.3.2 Data Selection and Description;183
10.3.3;7.3.3 Dynamic Extension of Network Slack-Based Measure DEA Model;184
10.4;7.4 Results and Discussions;187
10.4.1;7.4.1 Performance Analysis in Value-Creation Process;187
10.4.2;7.4.2 The Relationship Between Capital Stock and RandD Organizations Performance;190
10.5;7.5 Conclusions;192
10.6;References;193
11;Chapter 8: Evaluating Returns to Scale and Convexity in DEA Via Bootstrap: A Case Study with Brazilian Port Terminals;196
11.1;8.1 Introduction;196
11.2;8.2 Efficiency Measurement and RTS Characterization;198
11.2.1;8.2.1 Measuring Efficiency Scores Under Different Orientations and Frontiers;198
11.2.2;8.2.2 Scaling or RTS Characterization;201
11.2.3;8.2.3 Orientation Impact on RTS Characterization;202
11.3;8.3 Estimation and Bootstrapping in DEA;203
11.3.1;8.3.1 Estimation;203
11.3.2;8.3.2 Bootstrapping Method;205
11.4;8.4 Case Study: Brazilian Port Terminals;206
11.5;8.5 Results;210
11.5.1;8.5.1 Initial Estimates;210
11.5.2;8.5.2 Preliminary Statistics Tests on Initial Estimates;213
11.5.2.1;8.5.2.1 Testing for Model Specification;214
11.5.2.2;8.5.2.2 Testing for Differences Between Container and Bulk Terminals;214
11.5.2.3;8.5.2.3 Testing for Relevant Inputs and Outputs;215
11.5.2.4;8.5.2.4 Testing for Outliers;216
11.5.3;8.5.3 Bootstrapped Efficiency Scores and Convexity Assumption;217
11.5.4;8.5.4 RTS Characterizations: CIs for SI and uo;218
11.5.5;8.5.5 Discussion;220
11.6;8.6 Conclusions;220
11.7;References;221
12;9: DEA and Cooperative Game Theory;224
12.1;9.1 Introduction;224
12.2;9.2 Cooperative Game Theory;225
12.2.1;9.2.1 Bargaining Problems;225
12.2.1.1;9.2.1.1 The Nash Solution;226
12.2.1.2;9.2.1.2 The Kalai-Smorodinsky Solution;228
12.2.2;9.2.2 Transferable Utility Games;229
12.2.2.1;9.2.2.1 The Core and Related Concepts;230
12.2.2.2;9.2.2.2 The Shapley Value;230
12.2.2.3;9.2.2.3 The Least Core and the Nucleolus;232
12.3;9.3 Nash Bargaining Approaches to DEA;232
12.4;9.4 TU Cooperative Game Approaches to DEA;236
12.5;9.5 Further Potential Applications;239
12.5.1;9.5.1 Nash Decomposition for Process Efficiency in Multistage Production Systems;240
12.5.2;9.5.2 DEA Production Games;242
12.6;References;245
13;Chapter 10: Measuring Bank Performance: From Static Black Box to Dynamic Network Models;249
13.1;10.1 Introduction;250
13.2;10.2 Selective Literature Review;251
13.2.1;10.2.1 Network DEA and Dynamic DEA;251
13.2.2;10.2.2 Bank Production and Risk;253
13.3;10.3 Preliminaries;254
13.3.1;10.3.1 Black-Box Technology;254
13.3.2;10.3.2 Network Technology with Bad Outputs;255
13.3.3;10.3.3 Dynamic Technology with Carryovers;256
13.3.4;10.3.4 Dynamic-Network Technology;258
13.4;10.4 DEA Implementation;260
13.5;10.5 A Choice of Variables and Regulatory Constraints;268
13.5.1;10.5.1 Variable Selection: An Example;268
13.5.2;10.5.2 Imposing Bank Regulatory Constraint;269
13.6;10.6 A Summary;271
13.7;References;271
14;Chapter 11: Evaluation and Decomposition of Energy and Environmental Productivity Change Using DEA;275
14.1;11.1 Introduction;276
14.2;11.2 Luenberger Productivity Indicator and Its Decomposition;278
14.3;11.3 DEA Model for Energy and Environmental Efficiency Measurement;285
14.4;11.4 Application to China´s Regional Energy and Environmental Productivity Change;289
14.4.1;11.4.1 Data and Variables;290
14.4.2;11.4.2 Results and Discussions;293
14.5;11.5 Conclusions;303
14.6;References;304
15;Chapter 12: Identifying the Global Reference Set in DEA: An Application to the Determination of Returns to Scale;306
15.1;12.1 Introduction;307
15.1.1;Part I: On Identification of the Global Reference Set;308
15.1.2;Part II: On Determination of the RTS;310
15.2;12.2 Background;311
15.2.1;12.2.1 Technology Set;311
15.2.2;12.2.2 The RAM Model;312
15.3;12.3 Identifying the Global Reference Set (GRS);312
15.3.1;12.3.1 Definition of the GRS;312
15.3.2;12.3.2 Properties of the GRS;314
15.3.3;12.3.3 Identification of the GRS;316
15.3.4;12.3.4 Properties of the Proposed Approach;320
15.3.5;12.3.5 Numerical example;321
15.4;12.4 Determination of Returns to Scale (RTS);323
15.4.1;12.4.1 Definition of RTS for an Inefficient DMU;323
15.4.2;12.4.2 Determination of RTS Via the BCC Model;323
15.4.3;12.4.3 Determination of RTS Via the CCR Model;325
15.4.4;12.4.4 Numerical Example;326
15.4.4.1;12.4.4.1 Determining RTS Statuses of the DMUs Using Algorithm I;327
15.4.4.2;12.4.4.2 Determining RTS Statuses of the DMUs Using Algorithm II;327
15.5;12.5 Empirical Application;328
15.5.1;12.5.1 Evaluation of Schools via the RAM Model;329
15.5.2;12.5.2 Determining RTS Statuses of the Efficient Schools;329
15.5.3;12.5.3 Determining RTS Statuses of the Inefficient Schools;329
15.6;12.6 Summary and Concluding Remarks;333
15.7;References;334
16;Chapter 13: Technometrics Study Using DEA on Hybrid Electric Vehicles (HEVs);338
16.1;13.1 Introduction;339
16.2;13.2 Methodology;339
16.3;13.3 Research Model and Dataset;342
16.3.1;13.3.1 TFDEA Parameters;342
16.3.1.1;13.3.1.1 Input Variable;342
16.3.1.2;13.3.1.2 Output Variables;343
16.3.1.3;13.3.1.3 Categorical Parameter;344
16.3.2;13.3.2 Dataset;344
16.4;13.4 Analysis of the Technological Advancement Patterns;346
16.4.1;13.4.1 Two-Seaters and Compact Segments: ``Stagnated´´;347
16.4.2;13.4.2 Midsize Segment: ``Flourishing´´;348
16.4.3;13.4.3 Large Segment: ``Emerging´´;349
16.4.4;13.4.4 SUV Segment: ``Forging Ahead´´;351
16.4.5;13.4.5 Minivan Segment: ``Crossover´´;351
16.4.6;13.4.6 Pickup Truck Segment: ``Steady´´;352
16.5;13.5 Conclusion;352
16.6;Appendix: 2013 State-of-the-Art Frontiers of Different HEV Segments;353
16.7;References;354
17;Chapter 14: A Radial Framework for Estimating the Efficiency and Returns to Scale of a Multi-component Production System in DEA;357
17.1;14.1 Introduction;358
17.2;14.2 Radial Performance Measurement for a Multi-component System;360
17.2.1;14.2.1 Basic Model;361
17.2.2;14.2.2 Theoretical Connection with Black-Box Approach;363
17.3;14.3 Procedure for Estimating the Returns to Scale;368
17.4;14.4 Theoretical Connection Between Black Box Approach and Multi-component Approach;374
17.5;14.5 Application;375
17.5.1;14.5.1 Efficiency;376
17.5.2;14.5.2 Returns to Scale;381
17.6;14.6 Summary and Conclusion;382
17.7;Appendix;383
17.8;References;389
18;Chapter 15: DEA and Accounting Performance Measurement;391
18.1;15.1 Introduction;391
18.2;15.2 Accounting Information;392
18.3;15.3 Accounting Ratios for Performance Measurement;395
18.4;15.4 Accounting Information and Its Interpretation in Productivity Measurement;398
18.4.1;15.4.1 Model 1: Production Process;400
18.4.2;15.4.2 Model 2: Firm Financial Efficiency Model;401
18.4.3;15.4.3 Model 3: Funding Efficiency Model;401
18.5;15.5 Indexing Dollar Values and Translation of Foreign Currencies;402
18.6;15.6 Activity-Based Costing and DEA: Congenial Twins;404
18.7;15.7 DEA and the Balanced Scorecard: A New Approach to an Old Problem;408
18.8;15.8 Understanding Contextual Performance to ``Do Better´´;410
18.9;15.9 Summary;415
18.10;References;415
19;Chapter 16: DEA Environmental Assessment (I): Concepts and Methodologies;419
19.1;16.1 Introduction;420
19.2;16.2 Literature Review;422
19.3;16.3 Underlying Concepts for DEA Environmental Assessment;422
19.3.1;16.3.1 Abbreviations and nomenclatures;422
19.3.2;16.3.2 Natural and Managerial Disposability;423
19.3.3;16.3.3 Unification Between Natural and Managerial Disposability;424
19.3.4;16.3.4 Desirable Congestion (DC);426
19.4;16.4 Unified Efficiency;427
19.4.1;16.4.1 Unified Efficiency (UE);427
19.4.2;16.4.2 Unified Efficiency under Natural Disposability (UEN);430
19.4.3;16.4.3 Unified Efficiency under Managerial Disposability (UEM);431
19.4.4;16.4.4 Unified Efficiency under Natural and Managerial Disposability (UENM);432
19.4.5;16.4.5 Unified Efficiency under Natural and Managerial Disposability: UENM(DC) with a Possible Occurrence of Desirable Congest...;434
19.5;16.5 Investment Strategy;435
19.6;16.6 Empirical Study;436
19.7;16.7 Conclusion and Future Extensions;442
19.8;References;449
20;Chapter 17: DEA Environmental Assessment (II): A Literature Study;451
20.1;17.1 Introduction;452
20.2;17.2 DEA Environmental Assessment;453
20.3;17.3 Disposability Concepts;456
20.4;17.4 Electric Power Industry;462
20.5;17.5 Petroleum and Coal Industries;463
20.6;17.6 Agriculture, Fishery, Manufacturing and Transportation Industries;464
20.7;17.7 Economic Development and Corporate Strategy;465
20.8;17.8 Methodology Developments;466
20.9;17.9 Conclusion;468
20.10;References;469
21;Chapter 18: Corporate Environmental Sustainability and DEA;488
21.1;18.1 Introduction;488
21.2;18.2 Corporate Environmental Sustainability;489
21.3;18.3 Theory Testing and Statistical Inferencing with DEA: An Environmental Perspective;490
21.3.1;18.3.1 Financial and Environmental Performance Relationship;491
21.3.2;18.3.2 Ecological Efficiency and Technological Disposition Relationship;492
21.3.3;18.3.3 Environmental Practices, Performance and Risk Management;493
21.4;18.4 Benchmarking and Key Performance Indicators with DEA;494
21.5;18.5 Multiple Criteria Decision Making with DEA;496
21.5.1;18.5.1 Justifying and Choosing Environmental Technologies;497
21.6;18.6 Future Research Directions;498
21.7;18.7 Conclusion;500
21.8;References;501
22;Index;504